code: def get_rmse_log(net, X_train, y_train):
      """Gets root mse between the logarithms of the prediction and the truth."""
      num_train = X_train.shape[0]
      clipped_preds = np.clip(net(X_train), 1, float("inf"))
      return np.sqrt(
          2
          * np.sum(
              square_loss(np.log(clipped_preds), np.log(y_train))
          ).item()
          / num_train
      )
  
file_path: mxnet/example/gluon/house_prices/kaggle_k_fold_cross_validation.py
imports: import numpy
  import pandas
  from mxnet import autograd
  from mxnet import gluon
  from mxnet import np
label_desc: Use image node_id 1 for calling a global function with example usage: get_rmse_log(net, X_train, y_train) and returns: np